Open AccessStationary Time Series in Pricing
Author(s) -
Anna Anatolyevna Burdina,
Anna Aleksandrovekhrest,
Yuriy Nikolayevich Frolov,
Yelena Timofeyevna Manayenkova
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j1129.0881019
Subject(s) - exponential smoothing , series (stratigraphy) , computer science , autoregressive model , time series , artificial neural network , mathematical optimization , autoregressive integrated moving average , smoothing , exponential function , moving average , algorithm , econometrics , mathematics , artificial intelligence , machine learning , paleontology , mathematical analysis , computer vision , biology
The methods for analyzing the dynamics of time series are compared in this study, the mechanisms for assessing the accuracy of value forecasting are examined, and a brief description of the models and examples of their use are provided. The problem of choosing the optimal model according to the criterion of the minimum forecasting error is stated and solved. The methods of mathematical modeling, mathematical statistics and econometrics, such as autoregression, moving average, exponential smoothing, and neural network modeling were used to solve this problem. The result of the study is the algorithm for finding the optimal model based on minimizing the forecasting error, as well as the program that implements this algorithm.